SoroushVahidi/scidocs huggingface dataset:
ENHANCEMENT STRATEGIES FOR FRAME-TO-FRAME UAS STEREO VISUAL ODOMETRY
(2016)

Autonomous navigation of indoor unmanned aircraft systems (UAS) requires accurate pose estimations usually obtained from indirect measurements. Navigation based on inertial measurement units (IMU) is known to be affected by high drift rates. The incorporation of cameras provides complementary information due to the different underlying measurement principle. The scale ambiguity problem for monocular cameras is avoided when a light-weight stereo camera setup is used. However, also frame-to-frame stereo visual odometry (VO) approaches are known to accumulate pose estimation errors over time. Several valuable real-time capable techniques for outlier detection and drift reduction in frame-to-frame VO, for example robust relative orientation estimation using random sample consensus (RANSAC) and bundle adjustment, are available. This study addresses the problem of choosing appropriate VO components. We propose a frame-to-frame stereo VO method based on carefully selected components and parameters. This method is evaluated regarding the impact and value of different outlier detection and drift-reduction strategies, for example keyframe selection and sparse bundle adjustment (SBA), using reference benchmark data as well as own real stereo data. The experimental results demonstrate that our VO method is able to estimate quite accurate trajectories. Feature bucketing and keyframe selection are simple but effective strategies which further improve the VO results. Furthermore, introducing the stereo baseline constraint in pose graph optimization (PGO) leads to significant improvements. * Corresponding author
An Empirical Analysis of Deep Network Loss Surfaces
(2016)

The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions are of high dimension and non-convex and hence difficult to characterize. In this paper, we empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization methods. We do this through several experiments that are visualized on polygons to understand how and when these stochastic optimization methods find local minima.
Classification of Multiple-Sentence Questions
(2005)

Conventional QA systems cannot answer to the questions composed of two or more sentences. Therefore, we aim to construct a QA system that can answer such multiple-sentence questions. As the first stage, we propose a method for classifying multiple-sentence questions into question types. Specifically, we first extract the core sentence from a given question text. We use the core sentence and its question focus in question classification. The result of experiments shows that the proposed method improves F-measure by 8.8% and accuracy by 4.4%.
Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models
(2018)

Text mining the relations between chemicals and proteins is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical-protein relations in running text (PubMed abstracts). This manuscript describes our submission, which is an ensemble of three systems, including a Support Vector Machine, a Convolutional Neural Network, and a Recurrent Neural Network. Their output is combined using a decision based on majority voting or stacking. Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an f-score of 0.6410, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature. Our submission achieved the highest performance in the task during the 2017 challenge. Keywords—relation extraction; deep learning; chemical; protein
EasyConnect: A Management System for IoT Devices and Its Applications for Interactive Design and Art
(2015)

Many Internet of Things (IoT) technologies have been used in applications for money flow, logistics flow, people flow, interactive art design, and so on. To manage these increasing disparate devices and connectivity options, ETSI has specified end-to-end machine-to-machine (M2M) system architecture for IoT applications. Based on this architecture, we develop an IoT EasyConnect system to manage IoT devices. In our approach, an IoT device is characterized by its “features” (e.g., temperature, vibration, and display) that are manipulated by the network applications. If a network application handles the individual device features independently, then we can write a software module for each device feature, and the network application can be simply constructed by including these brick-like device feature modules. Based on the concept of device feature, brick-like software modules can provide simple and efficient mechanism to develop IoT device applications and interactions.
Inherent Resistance of Efficient ECC Designs against SCA Attacks
(2016)

The Montgomery kP algorithm using Lopez-Dahab projective coordinates is a time and energy efficient way to perform the elliptic curve point multiplication. It even can provide resistance against simple power analysis attacks. Nevertheless, straight forward implementations of this algorithm are not resistant against differential power analysis attacks. But highly-efficient designs i.e. designs in which different functional units are operating in parallel can increase the resistance of ECC implementations against DPA. In this paper we demonstrate this fact on the examples of our 6 implementations of the Montgomery kP algorithm. We perform horizontal DPA attacks to demonstrate the fact that the resistance of the kP algorithm against side channel analysis attacks can be increased through its implementation as an efficient design.
The Theory of Correlation Formulas and Their Application to Discourse Coherence
(2015)

The Winograd Schema Challenge (WSC) was proposed as a measure of machine intelligence. It boils down to anaphora resolution, a task familiar from computational linguistics. Research in linguistics and AI has coalesced around discourse coherence as the critical factor in solving this task, and the process of establishing discourse coherence relies fundamentally on world and commonsense knowledge. In this thesis, we build on an approach to establishing coherence on the basis of correlation. The utility of this approach lies in its conceptual clarity and ability to flexibly represent commonsense knowledge. We work to fill some conceptual holes with the Correlation Calculus approach. First, understanding the calculus in a vacuum is not straightfoward unless it has a precise semantics. Second, existing demonstrations of the Correlation Calculus on Winograd Schema Challenge problems have not been linguistically credible. We hope to ameliorate some—but by no means all—of the outstanding issues with the Correlation Calculus. We do so first by providing a precise semantics of the calculus, which relates our intuitive understanding of correlation with a precise notion involving probabilities. Second, we formulate the establishment of discourse coherence by correlation formulas within the framework of Discourse Representation Theory. This provides a more complete and linguistically credible account of the relationship between the Correlation Calculus, discourse coherence, and Winograd Schema Challenge problems.
Game-Related Statistics that Discriminated Winning, Drawing and Losing Teams from the Spanish Soccer League.
(2010)

The aim of the present study was to analyze men's football competitions, trying to identify which game-related statistics allow to discriminate winning, drawing and losing teams. The sample used corresponded to 380 games from the 2008-2009 season of the Spanish Men's Professional League. The game-related statistics gathered were: total shots, shots on goal, effectiveness, assists, crosses, offsides commited and received, corners, ball possession, crosses against, fouls committed and received, corners against, yellow and red cards, and venue. An univariate (t-test) and multivariate (discriminant) analysis of data was done. The results showed that winning teams had averages that were significantly higher for the following game statistics: total shots (p < 0.001), shots on goal (p < 0.01), effectiveness (p < 0.01), assists (p < 0.01), offsides committed (p < 0.01) and crosses against (p < 0.01). Losing teams had significantly higher averages in the variable crosses (p < 0.01), offsides received (p < 0. 01) and red cards (p < 0.01). Discriminant analysis allowed to conclude the following: the variables that discriminate between winning, drawing and losing teams were the total shots, shots on goal, crosses, crosses against, ball possession and venue. Coaches and players should be aware for these different profiles in order to increase knowledge about game cognitive and motor solicitation and, therefore, to evaluate specificity at the time of practice and game planning. Key pointsThis paper increases the knowledge about soccer match analysis.Give normative values to establish practice and match objectives.Give applications ideas to connect research with coaches' practice.
Free radicals and antioxidants in human health: current status and future prospects.
(2004)

Free radicals and related species have attracted a great deal of attention in recent years. They are mainly derived from oxygen (reactive oxygen species/ROS) and nitrogen (reactive nitrogen species/RNS), and are generated in our body by various endogenous systems, exposure to different physicochemical conditions or pathophysiological states. Free radicals can adversely alter lipids, proteins and DNA and have been implicated in aging and a number of human diseases. Lipids are highly prone to free radical damage resulting in lipid peroxidation that can lead to adverse alterations. Free radical damage to protein can result in loss of enzyme activity. Damage caused to DNA, can result in mutagenesis and carcinogenesis. Redox signaling is a major area of free radical research that is attracting attention. Nature has endowed us with protective antioxidant mechanisms- superoxide dismutase (SOD), catalase, glutathione, glutathione peroxidases and reductase, vitamin E (tocopherols and tocotrienols), vitamin C etc., apart from many dietary components. There are epidemiological evidences correlating higher intake of components/ foods with antioxidant abilities to lower incidence of various human morbidities or mortalities. Current research reveals the different potential applications of antioxidant/free radical manipulations in prevention or control of disease. Natural products from dietary components such as Indian spices and medicinal plants are known to possess antioxidant activity. Newer and future approaches include gene therapy to produce more antioxidants in the body, genetically engineered plant products with higher level of antioxidants, synthetic antioxidant enzymes (SOD mimics), novel biomolecules and the use of functional foods enriched with antioxidants.
Power LDMOS with novel STI profile for improved Rsp, BVdss, and reliability
(2010)

The profile of shallow trench isolation (STI) is designed to improve LDMOS specific on-resistance (Rsp), BVDSS, safe operating area (SOA), and hot carrier lifetimes (HCL) in an integrated BiCMOS power technology. Silicon etch, liner oxidation and CMP processes are tuned to improve the tradeoffs in a power technology showing significant improvement to both p-channel and n-channel Rsp compared to devices fabricated with the STI profile inherited from the original submicron CMOS platform. Extensive TCAD and experiments were carried out to gain insight into the physical mechanisms and further improve device performance after STI process optimization. The final process and device structures yield SOAs that are limited only by thermal constraints up to rated voltages
A Hygroscopic Sensor Electrode for Fast Stabilized Non-Contact ECG Signal Acquisition
(2015)

A capacitive electrocardiography (cECG) technique using a non-invasive ECG measuring technology that does not require direct contact between the sensor and the skin has attracted much interest. The system encounters several challenges when the sensor electrode and subject's skin are weakly coupled. Because there is no direct physical contact between the subject and any grounding point, there is no discharge path for the built-up electrostatic charge. Subsequently, the electrostatic charge build-up can temporarily contaminate the ECG signal from being clearly visible; a stabilization period (3-15 min) is required for the measurement of a clean, stable ECG signal at low humidity levels (below 55% relative humidity). Therefore, to obtain a clear ECG signal without noise and to reduce the ECG signal stabilization time to within 2 min in a dry ambient environment, we have developed a fabric electrode with embedded polymer (FEEP). The designed hygroscopic FEEP has an embedded superabsorbent polymer layer. The principle of FEEP as a conductive electrode is to provide humidity to the capacitive coupling to ensure strong coupling and to allow for the measurement of a stable, clear biomedical signal. The evaluation results show that hygroscopic FEEP is capable of rapidly measuring high-accuracy ECG signals with a higher SNR ratio.
A 3.9-ps RMS Precision Time-to-Digital Converter Using Ones-Counter Encoding Scheme in a Kintex-7 FPGA
(2017)

A 3.9-ps time-interval rms precision and 277-M events/second measurement throughput time-to-digital converter (TDC) is implemented in a Xilinx Kintex-7 field programmable gate array (FPGA). Unlike previous work, the TDC is achieved with a multichain tapped-delay line (TDL) followed by an ones-counter encoder. The four normal TDLs merged together make the TDC bins very small, so that the time precision can be significantly improved. The ones-counter encoder naturally applies global bubble error correction to the output of TDL, thus the TDC design is relatively simple even when using FPGAs made with current advanced process technology. The TDC implementation is a generally applicable method that can simultaneously achieve high time precision and high measurement throughput.
Comparative Evaluation of Background Subtraction Algorithms in Remote Scene Videos Captured by MWIR Sensors
(2017)

Background subtraction (BS) is one of the most commonly encountered tasks in video analysis and tracking systems. It distinguishes the foreground (moving objects) from the video sequences captured by static imaging sensors. Background subtraction in remote scene infrared (IR) video is important and common to lots of fields. This paper provides a Remote Scene IR Dataset captured by our designed medium-wave infrared (MWIR) sensor. Each video sequence in this dataset is identified with specific BS challenges and the pixel-wise ground truth of foreground (FG) for each frame is also provided. A series of experiments were conducted to evaluate BS algorithms on this proposed dataset. The overall performance of BS algorithms and the processor/memory requirements were compared. Proper evaluation metrics or criteria were employed to evaluate the capability of each BS algorithm to handle different kinds of BS challenges represented in this dataset. The results and conclusions in this paper provide valid references to develop new BS algorithm for remote scene IR video sequence, and some of them are not only limited to remote scene or IR video sequence but also generic for background subtraction. The Remote Scene IR dataset and the foreground masks detected by each evaluated BS algorithm are available online: https://github.com/JerryYaoGl/BSEvaluationRemoteSceneIR.
Can You Spot the Fakes?: On the Limitations of User Feedback in Online Social Networks
(2017)

Online social networks (OSNs) are appealing platforms for spammers and fraudsters, who typically use fake or compromised accounts to connect with and defraud real users. To combat such abuse, OSNs allow users to report fraudulent profiles or activity. The OSN can then use reporting data to review and/or limit activity of reported accounts. Previous authors have suggested that an OSN can augment its takedown algorithms by identifying a “trusted set” of users whose reports are weighted more heavily in the disposition of flagged accounts. Such identification would allow the OSN to improve both speed and accuracy of fake account detection and thus reduce the impact of spam on users. In this work we provide the first public, data-driven assessment of whether the above assumption is true: are some users better at reporting than others? Specifically, is reporting skill both measurable, i.e., possible to distinguish from random guessing; and repeatable, i.e., persistent over repeated sampling? Our main contributions are to develop a statistical framework that describes these properties and to apply this framework to data from LinkedIn, the professional social network. Our data includes member reports of fake profiles as well as the more voluminous, albeit weaker, signal of member responses to connection requests. We find that members demonstrating measurable, repeatable skill in identifying fake profiles do exist but are rare: at most 2.4% of those reporting fakes and at most 1.3% of those rejecting connection requests. We conclude that any reliable “trusted set” of members will be too small to have noticeable impact on spam metrics.
How to practice person‐centred care: A conceptual framework
(2018)

BACKGROUND
Globally, health-care systems and organizations are looking to improve health system performance through the implementation of a person-centred care (PCC) model. While numerous conceptual frameworks for PCC exist, a gap remains in practical guidance on PCC implementation.


METHODS
Based on a narrative review of the PCC literature, a generic conceptual framework was developed in collaboration with a patient partner, which synthesizes evidence, recommendations and best practice from existing frameworks and implementation case studies. The Donabedian model for health-care improvement was used to classify PCC domains into the categories of "Structure," "Process" and "Outcome" for health-care quality improvement.


DISCUSSION
The framework emphasizes the structural domain, which relates to the health-care system or context in which care is delivered, providing the foundation for PCC, and influencing the processes and outcomes of care. Structural domains identified include: the creation of a PCC culture across the continuum of care; co-designing educational programs, as well as health promotion and prevention programs with patients; providing a supportive and accommodating environment; and developing and integrating structures to support health information technology and to measure and monitor PCC performance. Process domains describe the importance of cultivating communication and respectful and compassionate care; engaging patients in managing their care; and integration of care. Outcome domains identified include: access to care and Patient-Reported Outcomes.


CONCLUSION
This conceptual framework provides a step-wise roadmap to guide health-care systems and organizations in the provision PCC across various health-care sectors.
SNOW 2014 Data Challenge: Assessing the Performance of News Topic Detection Methods in Social Media
(2014)

The SNOW 2014 Data Challenge aimed at creating a public benchmark and evaluation resource for the problem of topic detection in streams of social content. In particular, given a set of tweets spanning a time interval of interest, the Challenge required the extraction of the most significant news topics in short timeslots within the selected interval. Here, we provide details with respect to the Challenge definition, the data collection and evaluation process, and the results achieved by the 11 teams that participated in it, along with a concise retrospective analysis of the main conclusions and arising issues.
Sensing Trending Topics in Twitter
(2013)

Online social and news media generate rich and timely information about real-world events of all kinds. However, the huge amount of data available, along with the breadth of the user base, requires a substantial effort of information filtering to successfully drill down to relevant topics and events. Trending topic detection is therefore a fundamental building block to monitor and summarize information originating from social sources. There are a wide variety of methods and variables and they greatly affect the quality of results. We compare six topic detection methods on three Twitter datasets related to major events, which differ in their time scale and topic churn rate. We observe how the nature of the event considered, the volume of activity over time, the sampling procedure and the pre-processing of the data all greatly affect the quality of detected topics, which also depends on the type of detection method used. We find that standard natural language processing techniques can perform well for social streams on very focused topics, but novel techniques designed to mine the temporal distribution of concepts are needed to handle more heterogeneous streams containing multiple stories evolving in parallel. One of the novel topic detection methods we propose, based on -grams cooccurrence and topic ranking, consistently achieves the best performance across all these conditions, thus being more reliable than other state-of-the-art techniques.
Collective entity linking in web text: a graph-based method
(2011)

Entity Linking (EL) is the task of linking name mentions in Web text with their referent entities in a knowledge base. Traditional EL methods usually link name mentions in a document by assuming them to be independent. However, there is often additional interdependence between different EL decisions, i.e., the entities in the same document should be semantically related to each other. In these cases, Collective Entity Linking, in which the name mentions in the same document are linked jointly by exploiting the interdependence between them, can improve the entity linking accuracy.
 This paper proposes a graph-based collective EL method, which can model and exploit the global interdependence between different EL decisions. Specifically, we first propose a graph-based representation, called Referent Graph, which can model the global interdependence between different EL decisions. Then we propose a collective inference algorithm, which can jointly infer the referent entities of all name mentions by exploiting the interdependence captured in Referent Graph. The key benefit of our method comes from: 1) The global interdependence model of EL decisions; 2) The purely collective nature of the inference algorithm, in which evidence for related EL decisions can be reinforced into high-probability decisions. Experimental results show that our method can achieve significant performance improvement over the traditional EL methods.
Structural Architectures for a Deployable Wideband UHF Antenna
(2012)

This paper explores concepts for a wideband deployable antenna suitable for small satellites. The general approach taken was to closely couple antenna theory and structural mechanics, to produce a deployable antenna that is considered efficient in both fields. Approaches that can be deployed using stored elastic strain energy have been favored over those that require powered actuators or environmental effects to enforce deployment. Two types of concepts were developed: thin shell structure and pantograph. These concepts cover four antenna topologies: crossed log periodic, conical log spiral, helical and quadrifilar helix. Of these, the conical log spiral antenna and the accompanying deployment concepts are determined to be the most promising approaches that warrant further study.